Stochastic optimization based on principal-agent problem

By the theory of stochastic dynamic programming, we provide the methods for deriving the optimal rules. In this paper, we make two models in dynamic state process to maximize the expected utility of the agent and then obtain the famous Hamilton-Jacobi-Bellman equation. Furthermore, we derive explicit form solution and closed-form solution of the optimal equations for given utility functions.